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Background: Hemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death., Purpose: To accurately distinguish HBs from other cerebellar-and-brainstem tumors using a convolutional neural network model based on a contrast-enhanced brain MRI dataset., Study Type: Retrospective., Population: Four hundred five patients (182 = HBs; 223 = other cerebellar-and brainstem tumors): 305 cases for model training, and 100 for evaluation., Field Strength/sequence: 3 T/contrast-enhanced T1-weighted imaging (T1WI + C)., Assessment: A CNN-based 2D classification network was trained by using sliced data along the z-axis. To improve the performance of the network, we introduced demographic information, various data-augmentation methods and an auxiliary task to segment tumor region. Then, this method was compared with the evaluations performed by experienced and intermediate-level neuroradiologists, and the heatmap of deep feature, which indicates the contribution of each pixel to model prediction, was visualized by Grad-CAM for analyzing the misclassified cases., Statistical Tests: The Pearson chi-square test and an independent t-test were used to test for distribution difference in age and sex. And the independent t-test was exploited to evaluate the performance between experts and our proposed method. P value <0.05 was considered significant., Results: The trained network showed a higher accuracy for identifying HBs (accuracy = 0.902 ± 0.031, F1 = 0.891 ± 0.035, AUC = 0.926 ± 0.040) than experienced (accuracy = 0.887 ± 0.013, F1 = 0.868 ± 0.011, AUC = 0.881 ± 0.008) and intermediate-level (accuracy = 0.827 ± 0.037, F1 = 0.768 ± 0.068, AUC = 0.810 ± 0.047) neuroradiologists. The recall values were 0.910 ± 0.050, 0.659 ± 0.084, and 0.828 ± 0.019 for the trained network, intermediate and experienced neuroradiologists, respectively. Additional ablation experiments verified the utility of the introduced demographic information, data augmentation, and the auxiliary-segmentation task., Data Conclusion: Our proposed method can successfully distinguish HBs from other cerebellar-and-brainstem tumors and showed diagnostic efficiency comparable to that of experienced neuroradiologists., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas., (© 2021. The Author(s), under exclusive licence to United States and Canadian Academy of Pathology.)

Abstract: To explore the feasibility of using conventional MRI features combined with apparent diffusion coefficient (ADC) values for the differential diagnosis of testicular tumors.A total of 63 patients with pathologically confirmed testicular tumors were enrolled in this study. In particular, there were 46 cases of malignant lesions and 17 cases of benign lesions. All patients underwent conventional magnetic resonance imaging (MRI) and diffusion weighted imaging. Multivariate logistic regression models and receiver operating characteristic curves were constructed to assess diagnostic accuracies.T2-homogeneity, intratumoral septa, and peritumoral infiltration were more common in the malignant group, and capsule sign was more common in the benign group (P < .05 for all). The mean ADC value of the malignant group was lower than that of the benign group (P < .05). When the ADC value ≤ 0.90 × 10-3 mm2/s, the diagnosis tended to be malignancy. The conventional MRI model could achieve better diagnostic accuracy than ADC values alone (P < .05). Compared with the conventional MRI model, the specificity and accuracy of the full model (ADC and conventional MRI model) increased by 9.8% and 3.2%, respectively. T2-homogeneity and T2-hypointensity were more common in seminoma and lymphoma, cystic changes were more common in nonseminomatous germ cell tumor (NSGCT), and intratumoral septa was more common in seminoma (P < .05 for all). The ADC value of NSGCT was larger than seminoma, and lymphoma was the smallest (P < .05 for all). Cystic changes, T2-hypointensity, intratumoral septa, and ADC value were independent factors for differentiating the seminoma, NSGCT, and lymphoma subgroups.A combination of conventional MRI features and ADC values can improve the diagnostic efficiency for differentiating benign and malignant testicular tumors, and can additionally distinguish different subtypes of malignant testicular tumors., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: To develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns., Methods: A radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort., Findings: The radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016)., Interpretation: This study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients., Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

19 F magnetic resonance imaging ( 19 F MRI), a kind of non-invasive and non-radioactive diagnostic technique with no endogenous background signals, opens up new research avenues for accurate molecular imaging studies. However, 19 F MRI is manily limited by the performance of contrast agents. Here, for the first time, we presented the zwitterionic fluorinated polymer and nanogel as new types of superhydrophilic, sensitive and ultra-stable 19 F MRI contrast agents. The superhydrophilicity of carboxybetaine zwitterionic structure completely overcame the hydrophobic aggregation-induced signal attenuation associated with amphiphilic fluorinated polymer-based nanoprobes. In addition, the superhydrophilic contrast agent exhibited distinct advantages, including high 19 F-content (19.1 wt%), superior resistance to protein adsorption, constant MR properties and 19 F MRS-based quantitative determination in complex biological fluids, and intense 19 F MRI signals in the whole-body images after intravenous injection. In combination with angiogenesis targeting ligand, the superhydrophilic contrast agent was applied for the unambiguous detection of tumor. Importantly, computational algorithm was established for the directly quantitative determination of bioavailability and tumor-to-whole body ratio (TBR) from the in vivo 19 F MRI dataset, providing real-time information with non-invasive manner. Finally, crosslinked nanogels were developed with significantly prolonged systemic circulation, of which intense 19 F MRI signals nonspecifically distributed in the aortaventralis and blood-rich organs, instead of being trapped steadily in liver as with the state-of-the-art superhydrophobic perfluocarbon nanoemulsions. Overall, this kind of superhydrophilic, zwitterionic fluorinated polymer and nanogel could be defined as a new generation of high-performance 19 F MRI contrast agents, which hold great potential for image-based unambiguous disease detection and computational quantification., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: MR neurography(MRN) is an advanced imaging technique to visualize peripheral nerves. Our aim was to determine the value of morphological features of lumbosacral nerve roots on MRN in diagnosing chronic inflammatory demyelinating polyradiculoneuropathy(CIDP) and analyze their correlations with electrophysiological parameters., Methods: MRN of lumbosacral plexus was performed in 21 CIDP patients and 21 healthy volunteers. The cross-sectional areas(CSAs) and signal intensities(SI) of L3 to S1 nerve roots were measured and compared between two groups. Receiver operating characteristic(ROC) curves were plotted to assess the diagnostic accuracy. All patients also underwent nerve conduction studies. Correlations between CSAs and SI of lumbosacral nerve roots and electrophysiological parameters were analyzed., Results: Compared with control group, CIDP patients showed significantly increased CSAs and SI from L3 to S1 nerve root (P < 0.001 and P < 0.05 respectively for all nerve roots). The CSA mean and SI mean were 28.04 ± 8.55mm 2 , 1.314 ± 0.199 for patient group and 14.91 ± 2.36mm 2 ,1.155 ± 0.094 for control group. ROC analysis revealed the best diagnostic accuracy for the CSA mean with an area under the curve of 0.968 and optimal cut-off value of 19.20 mm 2 . CSAs of L5 or S1 nerve root correlated positively with central latency and negatively with conduction velocity of tibial nerve. SI of L5 also had a positive correlation with latency of sural nerve., Conclusions: Evaluation of lumbosacral nerve roots on MRN in a quantitative manner may serve as an important tool to support the diagnosis of CIDP., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

To study whether some of the quantitative enhancement and necrosis features in preoperative conventional MRI (cMRI) had a predictive value for epidermal growth factor receptor (EGFR) gene amplification status in glioblastoma multiforme (GBM).Fifty-five patients with pathologically determined GBMs who underwent cMRI were retrospectively reviewed. The following cMRI features were quantitatively measured and recorded: long and short diameters of the enhanced portion (LDE and SDE), maximum and minimum thickness of the enhanced portion (MaxTE and MinTE), and long and short diameters of the necrotic portion (LDN and SDN). Univariate analysis of each feature and a decision tree model fed with all the features were performed. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of features, and predictive accuracy was used to assess the performance of the model.For single feature, MinTE showed the best performance in differentiating EGFR gene amplification negative (wild-type) (nEGFR) GBM from EGFR gene amplification positive (pEGFR) GBM, and it got an AUC of 0.68 with a cut-off value of 2.6 mm. The decision tree model included 2 features MinTE and SDN, and got an accuracy of 0.83 in validation dataset.Our results suggest that quantitative measurement of the features MinTE and SDN in preoperative cMRI had a high accuracy for predicting EGFR gene amplification status in GBM.

An antibody-directed nonviral vector, polyethylene glycol-grafted polyethylenimine functionalized with superparamagnetic iron oxide nanoparticles and a gastric cancer-associated CD44v6 single-chain variable fragment (scFv(CD44v6),-PEG-g-PEI-SPION), was constructed as a gastric cancer-targeting and magnetic resonance imaging (MRI)-visible nanocarrier for small interfering RNA (siRNA) delivery. Biophysical characterization of PEG-g-PEI-SPION and scFv(CD44v6)-PEG-g-PEI-SPION was carried out, including siRNA condensation capacity, cell viability, and transfection efficiency. Both the targeting and nontargeting nanocarriers were effective for transferring siRNA in vitro. The cellular uptake and distribution of nanoparticles complexed with siRNA was analyzed by fluorescence imaging and immunofluorescent staining. Moreover, the gastric cancer-targeting effect was verified in vivo by MRI and histology analysis. These results indicate that scFv(CD44v6)-PEG-g-PEI-SPION is a promising nonviral vector for gastric cancer gene therapy and diagnosis.

Objective: To observe the different cerebral activations between pre-administration and post-administration of tryptophan-depleted amino acid mixture (TRP-)/balanced amino acid mixture (BAL) in healthy volunteers through functional magnetic resonance imaging (fMRI) when they received electrical stimulation at the same time. Furthermore, the effects of 5-hydroxytryptamine (5-HT) were explored in pain modulation network., Methods: Two-phase crossover design was adopted. Six right-handed healthy volunteers participated in the experiment. TRP- and BAL were administered to each participant according to the Young's method. Volunteers were scanned under fMRI with or without administration of TRP- or BAL when receiving electrical stimulation simultaneously. Analysis of functional neuroimages (AFNI) software was used to show the different activations in the human brain between pre- and post-administration of TRP-/BAL. Then all the results were analyzed by SPSS 14.0., Results: (1) Bilateral primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were activated during pre-administration and post-administration of TRP-, but the activated volumes of bilateral SI and SII during post-administration of TRP- were found significantly larger than that during pre-administration of TRP (P<0.05). (2) All the activated regions showed no difference between pre- and post-administration of BAL. (3) There were significant differences of pain threshold between pre- and post-administration of TRP- (P<0.01), which meant that pain threshold reduced remarkably after TRP- administration, while it showed no difference between pre- and post-administration of BAL (P>0.05)., Conclusion: 5-HT plays an important role in pain modulation in the central nervous system, TRP- causes acute tryptophan depletion (ATD) through reducing the level of 5-HT in the human brain, which can induce the decrease of volunteers' pain threshold. The activated volumes of bilateral SI and SII display significant difference between pre- and post-administration of TRP-, suggesting these brain areas may not only involve in the 5-HT related activities but in pain modulation network as well.

Background: Genetic testing for molecular markers of gliomas sometimes is unavailable because of time‐consuming and expensive, even limited tumor specimens or nonsurgery cases. Purpose: To train a three‐class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q‐noncodeleted (IDHmut‐noncodel), IDH wild‐type (IDHwt), IDH‐mutant and 1p/19q‐codeleted (IDHmut‐codel) of adult gliomas and investigate whether radiomic features from diffusion‐weighted imaging (DWI) could bring additive value. Study Type: Retrospective. Population: A total of 755 patients including 111 IDHmut‐noncodel, 571 IDHwt, and 73 IDHmut‐codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut‐noncodel, 104 IDHwt, and 14 IDHmut‐codel cases were utilized as external validation set. Field Strength/Sequence: A 1.5 T or 3.0 T/multiparametric MRI, including T1‐weighted (T1), T1‐weighted gadolinium contrast‐enhanced (T1c), T2‐weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. Assessment: The performance of multiparametric radiomic model (random‐forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. Statistical Tests: Mann–Whitney U test, Chi‐Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. Results: The multiparametric radiomic model achieved AUC values for IDHmut‐noncodel, IDHwt, and IDHmut‐codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut‐noncodel subtypes. Data Conclusion: Radiomic features from DWI could bring additive value and improve the performance of conventional MRI‐based radiomic model for classifying the molecular subtypes especially IDHmut‐noncodel and IDHwt of adult gliomas. Level of Evidence: 3. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Background: Stiffness of meningioma is an important factor affecting the surgical resection and the prognosis of patients. Purpose: To examine the feasibility of APTw‐magnetic resonance imaging (MRI) in evaluating meningioma stiffness. Study Type: Retrospective. Population: Seventy‐one patient with meningiomas, 39 were male and 32 were female; the mean age was 51 ± 10 years. Field Strength/Sequence: 3.0T; Turbo‐spin‐echo T1‐weighted and Gd‐T1‐weighted sequence; Turbo‐spin‐echo T2‐weighted sequence; 2D fat‐suppressed, turbo‐spin‐echo APTw pulse sequence. Assessment: The T1WI signal intensity score, T2WI signal intensity score, APTwmin, APTwmax, and APTwmean values were compared between soft, medium stiff and stiff meningiomas or non‐stiff meningiomas and stiff meningiomas group. Statistical Tests: Chi‐square test, one‐way ANOVA analysis, independent‐samples t‐test, intra‐class correlation coefficient, rank‐sum test, receiver operating characteristic curve analysis. P < 0.05 was considered statistically significant in all tests. Results: APTwmin and APTwmean in the stiff group were significantly lower than that in the non‐stiff group (2.79% ± 0.42% vs. 1.90% ± 0.60% and 3.20% ± 0.31% vs. 2.55% ± 0.61%). APTwmin and APTwmean in the stiff group were significantly lower than that in the medium stiff and soft groups (1.90% ± 0.60% vs. 2.69% ± 0.40% and 3.12% ± 0.32%, 2.55% ± 0.61% vs. 3.17% ± 0.33% and 3.39% ± 0.18%), APTwmin in the medium stiff group was significantly lower than in the soft group, there was no significant difference in APTwmean between the medium stiff and soft groups (P = 0.190). APTwmin showed the best diagnostic performance for evaluating meningioma stiffness with an area under the curve of 0.913, when the APTwmin was lower than 2.4%, the meningioma was defined as a stiff tumor, the sensitivity, specificity, and accuracy were 87.1%, 87.5%, and 85.9%, respectively. Data Conclusion: APTw‐MRI could be used to evaluate meningioma stiffness, with APTwmin having the best evaluative efficiency. Level of Evidence: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background: Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice. Purpose: To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer. Material and Methods: A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t -test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed. Results: The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004). Conclusion: IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. Methods: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. Results: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). Conclusions: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. Key Points: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated. [ABSTRACT FROM AUTHOR]

Purpose: To explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer. Materials and Methods: A total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results. Results: D* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10−3 mm2/s and MK ≥ 0.69, the NPI tended to be high grade (with areas under the curve (AUCs) of 0.712 and 0.647, respectively). The combination of D* and MK demonstrated the highest AUC of 0.734 in grading NPI with sensitivity and accuracy of 71.7% and 77.1%, respectively. Additionally, higher D*, f, and MK and lower ADC and D values were observed in the high Ki-67 than low Ki-67 expression groups (p < 0.05). The AUC of the combined model (D + D* + f + MK) was 0.755, being significantly higher than that of single parameters (Z = 2.770~3.244, p = 0.001~0.006) in distinguishing high from low Ki-67 expression. D* and f values in the Luminal A subtype were significantly lower than in other subtypes (p < 0.05). Luminal B showed decreased D value compared with other subtypes (p < 0.05). The HER-2-positive subtype demonstrated increased ADC values compared with the Luminal B subtype (p < 0.05). Luminal A/B showed significantly lower D, D*, MD, and MK than the non-Luminal subtypes (p < 0.05). The combined model (D + D* + MD + MK) showed an AUC of 0.830 in diagnosing the Luminal and non-Luminal subtypes, which is significantly higher than that of a single parameter (Z = 3.273~4.440, p < 0.01). f ≥ 54.30% [odds ratio (OR) = 1.038, p < 0.001] and MK ≥ 0.68 (OR = 24.745, p = 0.012) were found to be significant predictors of triple-negative subtypes. The combination of f and MK values demonstrated superior diagnostic performance with AUC, sensitivity, specificity, and accuracy of 0.756, 67.5%, 77.5%, and 82.4%, respectively. Moreover, as shown in the calibration curve, strong agreements were observed between nomogram prediction probability and actual findings in the prediction of genotypes (p = 0.22, 0.74). Conclusion: DWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment. [ABSTRACT FROM AUTHOR]

Background: Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors. Methods: A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built. Results: The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively. Conclusion: The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance. [ABSTRACT FROM AUTHOR]

At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) images can predict clinical outcome of patients with AIS. Patients with AIS were divided into a training (n = 110) and an external validation (n = 80) sets. A total of 753 radiomics features were extracted from each FLAIR and ADC image of the 190 patients. Interquartile range (IQR), Wilcoxon rank sum test, and least absolute shrinkage and selection operator (LASSO) were used to reduce the feature dimension. The six strongest radiomics features were related to an unfavorable outcome of AIS. A logistic regression analysis was employed for selection of potential predominating clinical and conventional magnetic resonance imaging (MRI) factors. Subsequently, we developed several models based on clinical and conventional MRI factors and radiomics features to predict the outcome of AIS patients. For predicting unfavorable outcome [modified Rankin scale (mRS) > 2] in the training set, the area under the receiver operating characteristic curve (AUC) of ADC radiomics model was 0.772, FLAIR radiomics model 0.731, ADC and FLAIR radiomics model 0.815, clinical model 0.791, and clinical and conventional MRI model 0.782. In the external validation set, the AUCs for the prediction with ADC radiomics model was 0.792, FLAIR radiomics model 0.707, ADC and FLAIR radiomics model 0.825, clinical model 0.763, and clinical and conventional MRI model 0.751. When adding radiomics features to the combined model, the AUCs for predicting unfavorable outcome in the training and external validation sets were 0.926 and 0.864, respectively. Our results indicate that the radiomics features extracted from FLAIR and ADC can be instrumental biomarkers to predict unfavorable clinical outcome of AIS and would additionally improve predictive performance when adding to combined model. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively assess the blood oxygen levels of the cerebral vein using quantitative susceptibility mapping (QSM), and to analyze the correlation between magnetic susceptibility value (MSV) and clinical laboratory indicators/cognitive scores in patients with Alzheimer's disease (AD). Materials and Methods: Fifty-nine patients (21 males and 38 females) with clinically confirmed AD (AD group) and 22 control subjects (12 males, 10 females; CON group) were recruited. Clinical data and laboratory examination indexes were collected. All patients underwent Mini-mental State Examination, Montreal Cognitive Assessment, Clock Drawing Task, and Activity of Daily Living Scale test, as well as a routine MRI and enhanced gradient echo T2 star weighted angiography (ESWAN). Results: Higher cerebral venous MSV was observed in AD group compared to CON group, significant differences were observed for bilateral thalamus veins and left dentate nucleus veins. The MSV of bilateral thalamus veins, bilateral internal cerebral veins, and bilateral dentate nucleus veins had significant negative correlation with Mini-mental State Examination score; the MSV of bilateral thalamus veins, bilateral dentate nucleus veins, right septal vein had a significant negative correlation with Montreal Cognitive Assessment scores; a significant negative correlation between the MSV of bilateral thalamus veins, left dentate nucleus vein, right septal vein and the Clock Drawing Task score; the MSV of bilateral thalamus veins, left dentate nucleus vein had a significant negative correlation with Activity of Daily Living Scale score. The MSV of left dentate nucleus vein was positively correlated with the course of the disease, the MSV of bilateral septal vein were positively correlated with the total cholesterol, and the MSV of left septal vein had a positive correlation with LDL. Conclusion: Decreasing cerebral venous oxygen level in AD patients may affect cognitive status, and associated with the deterioration of the disease in AD patients. [ABSTRACT FROM AUTHOR]

The high reactive oxygen species (ROS) generation ability and simple construction of sonosensitizer systems remain challenging in sonodynamic therapy against the hypoxic tumor. In this work, we rationally prepared MOF‐derived double‐layer hollow manganese silicate nanoparticle (DHMS) with highly effective ROS yield under ultrasound irradiation for multimodal imaging‐guided sonodynamic therapy (SDT). The presence of Mn in DHMS increased ROS generation efficiency because it could be oxidized by holes to improve the electron–hole separation. Moreover, DHMS could produce oxygen in the tumor microenvironment, which helps overcome the hypoxia of the solid tumor and thus enhance the treatment efficiency. In vivo experiments demonstrated efficient tumor inhibition in DHMS‐mediated SDT guided by ultrasound and magnetic resonance imaging. This work presents a MOF‐derived nanoparticle with sonosensitive and oxygen generating ability, which provides a promising strategy for tumor hypoxia in SDT. [ABSTRACT FROM AUTHOR]

Background Anterior Gradient (AGR) 2 concentration increases in the serum of tumor patients, and their diagnostic and prognostic significances were evaluated in some tumors. The previous works showed that AGR2 high express in nasopharyngeal carcinoma (NPC) biopsy tissues. However, whether AGR2 serves as a diagnostic and prognostic marker for NPC remains unclear. Methods 42 healthy volunteers, 34 breast cancer patients and 124 NPC patients were enrolled into this study, and the serum samples were collected from these healthy volunteers, breast cancer patients and NPC patients. Concomitantly, 79 frozen nasopharyngeal specimens consisted of 65 NPC tissues and 14 normal nasopharyngeal tissues were enrolled in the observation. The enzyme linked immunosorbent assay (ELISA) was used to estimate AGR2 concentration in the serum samples, and AGR2 mRNA expressions in the frozen tissue samples were detected by real time RT-PCR. Results The real time RT-PCR results showed that AGR2 mRNA level was increased in NPC tissues compared with the normal nasopharyngeal tissues ( p < 0.05). The ELISA data showed that AGR2 concentration in NPC serum was significantly higher in NPC patient serums than that in the health population ( p < 0.05). And, AGR2 expression showed a correlation with tumor node metastasis (TNM) grade ( p < 0.05) and Recurrence ( p < 0.05). Moreover, the cumulative survival rate of patients with high concentration of AGR2 was significantly lower than that of patients with low concentration of AGR2 ( p < 0.05), and the cumulative hazard rate of patients with high concentration of AGR2 was significantly higher than that with low concentration of AGR2 ( p < 0.05). Conclusion Serum AGR2 can be used as a serum marker for clinical prognosis of nasopharyngeal carcinoma. However, serum AGR2 levels could not provide advantages in clinical practice for the differential diagnosis of cancer. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of a single-stage tempering treatment during microwave vacuum drying (MVD) on drying characteristics and quality of lotus (Nelumbo nuciferaGaertn.) seeds using two parameters: intermediate moisture content (IMC) and tempering temperature (4 and 25°C). Magnetic resonance imaging (MRI) was used to examine moisture migration and distribution in individual lotus seed during tempering. Results from MRI showed tempering could reduce the moisture gradient in lotus seeds during MVD. The tempering treatments led to increased moisture diffusivity (3.96–43.56%) and a shortened drying time (6.25–31.25%) when compared with continuous MVD. Furthermore, tempered samples exhibited a greater rehydration capacity, a limited overall color change, and increased amounts of taste-active amino acids when compared with nontempered lotus seeds. High IMC improved rehydration ratios of dried samples. Low tempering temperature provided favorable free amino acid content and desirable product color. [ABSTRACT FROM PUBLISHER]

Purpose: Knowledge of dielectric properties of malignant human tissues is necessary for the recently developed magnetic resonance (MR) technique called MR electrical property tomography. This technique may be used in early tumor detection based on the obvious differentiation of the dielectric properties between normal and malignant tissues. However, the dielectric properties of malignant human tissues in the scale of the Larmor frequencies are not completely available in the literature. In this study, the authors focused only on the dielectric properties of colorectal tumor tissue. Methods: The dielectric properties of 504 colorectal malignant samples excised from 85 patients in the scale of the Larmor frequencies were measured using the precision open-ended coaxial probe method. The obtained complex-permittivity data were fitted to the single-pole Cole-Cole model. Results: The median permittivity and conductivity for the malignant tissue sample were 79.3 and 0.881 S/m at 128 MHz, which were 14.6% and 17.0% higher, respectively, than those of normal tissue samples. Significant differences between normal and malignant tissues were found for the dielectric properties (p < 0.05). Conclusions: Experimental results indicated that the dielectric properties were significantly different between normal and malignant tissues for colorectal tissue. This large-scale clinical measurement provides more subtle base data to validate the technique of MR electrical property tomography. [ABSTRACT FROM AUTHOR]

Purpose: To explore the clinical value of diffusional kurtosis imaging (DKI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting genotypes and prognostic factors of breast cancer.Materials and Methods: A total of 130 female patients with pathologically-confirmed breast cancer and DKI and DCE-MRI data were reviewed retrospectively. Two radiologists independently evaluated mean diffusivity (MD) and mean kurtosis (MK) for the DKI model and volume transfer constant (Ktrans), reverse rate constant (Kep), and extracellular extravascular volume ratio (Ve) for the DCE-MRI model for post-processing analyses. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic efficacies.Results: MK, Ktrans, and Kep values were significantly higher in the high-grade Nottingham prognostic index (NPI) group (NPI ≥ 3.4) than in the low-grade NPI group (NPI < 3.4) (p < 0.01). The Ktrans had significantly greater area under ROC curve (AUC) than Kep and MK in predicting the NPI (p = 0.038 and 0.0217, respectively). Higher Ktrans, Kep, and MK values were observed in the high Ki-67 expression (≥14%) group than in the low Ki-67 expression (<14%) group (p < 0.05). Moreover, the MK value had better diagnostic performance than the Ktrans and Kep values in identifying Ki-67 expression status (p = 0.0097 and 0.0008, respectively). The combined model (MD + MK + Ktrans + Ve) had a significantly higher AUC than the single parameters for differentiating between luminal A/B and non-luminal subtypes (p = 0.003, < 0.001, 0.001, and < 0.001, respectively). The Human epidermal growth factor receptor 2-positive group had higher MD and Ve values than the other subtype groups (p < 0.05), and the Ve had a sensitivity of 100%. Moreover, the Ve AUC was significantly higher than that for MD in the identification of the triple-negative subtype (p = 0.048).Conclusion: Ktrans of DCE-MRI and MK of DKI demonstrated good diagnostic performance in predicting the prognostic factors of breast cancer. Additionally, the combination of the DCE-MRI and DKI models can improve the efficiency of predicting breast cancer genotypes. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region.Materials and Methods: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated.Results: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable.Conclusion: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas. [ABSTRACT FROM AUTHOR]

Objective: To investigate the CT and MR features of "inferior vena cava(IVC) reverse-flow" sign and "jet-blood" sign in Budd-Chiari Syndrome (BCS).Methods: The liver CT and/or MRI plain scan and dynamic enhancement of 107 cases of BCS diagnosed by DSA and/or clinic were collected, including 17 patients with hepatic vein obstruction type, 79 patients with IVC obstruction type, and 11 patients with mixed type. The manifestations of IVC reverse-flow sign and jet-blood sign in the latter two type BCS (90cases) imaging were analyzed.Results: 1) The incidence of IVC reverse-flow sign in the IVC obstruction type and mixed type was 93.3 %(83/90), which was manifested as: The contrast agent was shown below the level of renal veins in the hepatic arterial phase enhancement, while no contrast agent was shown above it at the same time. 2) The incidence of jet-blood sign in membrane-perforated subtype was 100 %(15/15) or 16.7 %(15/90), which was manifested as: The low density/signal dots appeared within full of contrast agent at the superior liver segment IVC on axial slices in arterial phase of CT/MRI enhancement, or the low signal line within agent presented above the IVC membrane on coronal image in equilibrium phase of MR enhancement. 3) Other imaging findings: 75 cases of membrane, 18 cases of membrane calcification, 27 cases of abnormal hepatic vein, 76 cases of abnormal structure of IVC, 18 cases of thrombus/cancer embolus in hepatic vein and/or IVC. There were 65 cases of abnormal liver appearance; 71 cases of abnormal liver enhancement; 21 cases of hepatic vein unsynchronous enhancement. Anastomotic branches among the hepatic veins open with comma sign in 54 cases; dilatation of intraspinal and paravertebral veins, azygos and hemiazygos veins in 90 cases(100 %, 90/90) or 96 cases(89.7 %, 96/107).Conclusions: The "IVC reverse-flow" sign is a specific CT and MR sign of IVC type and mixed type BCS, and the "jet-blood" sign is a characteristic CT and MR sign of membrane-perforated subtype BCS. [ABSTRACT FROM AUTHOR]